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Colorectal cancer (CRC) is the third most diagnosed cancer and the second deadliest cancer worldwide representing a major public health problem. In recent years, increasing evidence has shown that microRNA (miRNA) can control the expression of targeted human messenger RNA (mRNA) by reducing their abundance or translation, acting as oncogenes or tumor suppressors in various cancers, including CRC. Due to the significant up-regulation of oncogenic miRNAs in CRC, elucidating the underlying mechanism and identifying dysregulated miRNA targets may provide a basis for improving current therapeutic interventions. In this paper, we proposed Gra-CRC-miRTar, a pre-trained nucleotide-to-graph neural network framework, for identifying potential miRNA targets in CRC. Different from previous studies, we constructed two pre-trained models to encode RNA sequences and transformed them into de Bruijn graphs. We employed different graph neural networks to learn the latent representations. The embeddings generated from de Bruijn graphs were then fed into a Multilayer Perceptron (MLP) to perform the prediction tasks. Our extensive experiments show that Gra-CRC-miRTar achieves better performance than other deep learning algorithms and existing predictors. In addition, our analyses also successfully revealed 172 out of 201 functional interactions through experimentally validated miRNA-mRNA pairs in CRC. Collectively, our effort provides an accurate and efficient framework to identify potential miRNA targets in CRC, which can also be used to reveal miRNA target interactions in other malignancies, facilitating the development of novel therapeutics. The Gra-CRC-miRTar web server can be found at: http://gra-crc-mirtar.com/.

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BACKGROUND: Radiomics provided opportunities to quantify the tumor phenotype non-invasively. This study extracted contrast-enhanced computed tomography (CECT) radiomic signatures and evaluated clinical features of bone metastasis in non-small-cell lung cancer (NSCLC). With the combination of the revealed radiomics and clinical features, the predictive modeling on bone metastasis in NSCLC was established. METHODS: A total of 318 patients with NSCLC at the Tianjin Medical University Cancer Institute & Hospital was enrolled between January 2009 and December 2019, which included a feature-learning cohort (n = 223) and a validation cohort (n = 95). We trained a radiomics model in 318 CECT images from feature-learning cohort to extract the radiomics features of bone metastasis in NSCLC. The Kruskal-Wallis and the least absolute shrinkage and selection operator regression (LASSO) were used to select bone metastasis-related features and construct the CT radiomics score (Rad-score). Multivariate logistic regression was performed with the combination of the Rad-score and clinical data. A predictive nomogram was subsequently developed. RESULTS: Radiomics models using CECT scans were significant on bone metastasis prediction in NSCLC. Model performance was enhanced with each information into the model. The radiomics nomogram achieved an AUC of 0.745 (95% confidence interval [CI]: 0.68,0.80) on predicting bone metastasis in the training set and an AUC of 0.808(95% confidence interval [CI]: 0.71,0.88) in the validation set. CONCLUSION: The revealed invisible image features were of significance on guiding bone metastasis prediction in NSCLC. Based on the combination of the image features and clinical characteristics, the predictive nomogram was established. Such nomogram can be used for the auxiliary screening of bone metastasis in NSCLC.

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MOTIVATION: Transcription factors are pivotal in the regulation of gene expression, and accurate identification of transcription factor binding sites (TFBSs) at high resolution is crucial for understanding the mechanisms underlying gene regulation. The task of identifying TFBSs from DNA sequences is a significant challenge in the field of computational biology today. To address this challenge, a variety of computational approaches have been developed. However, these methods face limitations in their ability to achieve high-resolution identification and often lack interpretability. RESULTS: We propose BertSNR, an interpretable deep learning framework for identifying TFBSs at single-nucleotide resolution. BertSNR integrates sequence-level and token-level information by multi-task learning based on pre-trained DNA language models. Benchmarking comparisons show that our BertSNR outperforms the existing state-of-the-art methods in TFBS predictions. Importantly, we enhanced the interpretability of the model through attentional weight visualization and motif analysis, and discovered the subtle relationship between attention weight and motif. Moreover, BertSNR effectively identifies TFBSs in promoter regions, facilitating the study of intricate gene regulation. AVAILABILITY AND IMPLEMENTATION: The BertSNR source code can be found at https://github.com/lhy0322/BertSNR.

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Curcumin is a natural polyphenolic compound extracted from turmeric with antibacterial, antioxidant, antitumor, preventive and therapeutic neurological disorders and a variety of bioactivities, which is widely used in the field of food and medicine. However, the drawbacks of curcumin such as poor aqueous solubility and stability have limited the practical application of curcumin. To overcome these defects and enhance its functional properties, various nanoscale systems (liposomes, polymer nanoparticles, protein nanoparticles, solid lipid nanoparticles, metal nanoparticles, etc) have been extensively employed for curcumin encapsulation and delivery. Despite the rapid development of curcumin nanoformulations, there is a lack of comprehensive reviews on their preparation and properties. This review provides an overview of the construction of curcumin nano-delivery systems, mechanisms of action, nanocarrier preparation methods and the applications of curcumin nanocarriers in the food and pharmaceutical fields to provide a theoretical basis and technological support for the efficient bio-utilization, product development and early clinical application of curcumin.

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Flexible thermoelectric materials have drawn significant attention from researchers due to their potential applications in wearable electronics and the Internet of Things. Despite many reports on these materials, it remains a significant challenge to develop cost-effective methods for large-scale, patterned fabrication of materials that exhibit both excellent thermoelectric performance and remarkable flexibility. In this study, we have developed an Ag2Se-based ink with excellent printability that can be used to fabricate flexible thermoelectric films by screen printing and low-temperature sintering. The printed films exhibit a Seebeck coefficient of -161 µV/K and a power factor of 3250.9 µW/m·K2 at 400 K. Moreover, the films demonstrate remarkable flexibility, showing minimal changes in resistance after being bent 5000 times at a radius of 5 mm. Overall, this research offers a new opportunity for the large-scale patterned production of flexible thermoelectric films.

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Simple and rapid molecular detection technologies for authenticating animal species are urgently needed for food safety and authenticity. This study established a new direct-fast quantitative polymerase chain reaction (qPCR) detection technology for beef to achieve rapid and on-site nucleic acid detection in food. This technology can complete nucleic acid extraction in 4 min using a new type of food nucleic acid-releasing agent, followed by direct amplification of the DNA sample by fast qPCR in 25 min. The results indicated that direct-fast qPCR can specifically identify beef and can also identify 0.00001% of beef components in artificially simulated meat mixtures, with a detection precision variation coefficient of <4%. This method can be used to effectively identify beef in different food samples. As a simple, fast, and accurate molecular detection technology for beef, this method may provide a new tool for the on-site detection of beef components in food.

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Merkel cell carcinoma (MCC) is an aggressive neuroendocrine skin cancer with a ∼50% response rate to immune checkpoint blockade (ICB) therapy. To identify predictive biomarkers, we integrated bulk and single-cell RNA sequencing (RNA-seq) with spatial transcriptomics from a cohort of 186 samples from 116 patients, including bulk RNA-seq from 14 matched pairs pre- and post-ICB. In nonresponders, tumors show evidence of increased tumor proliferation, neuronal stem cell markers, and IL1. Responders have increased type I/II interferons and preexisting tissue resident (Trm) CD8 or Vδ1 γδ T cells that functionally converge with overlapping antigen-specific transcriptional programs and clonal expansion of public T-cell receptors. Spatial transcriptomics demonstrated colocalization of T cells with B and dendritic cells, which supply chemokines and costimulation. Lastly, ICB significantly increased clonal expansion or recruitment of Trm and Vδ1 cells in tumors specifically in responders, underscoring their therapeutic importance. These data identify potential clinically actionable biomarkers and therapeutic targets for MCC. Significance: MCC serves as a model of ICB response. We utilized the largest-to-date, multimodal MCC dataset (n = 116 patients) to uncover unique tumor-intrinsic properties and immune circuits that predict response. We identified CD8 Trm and Vδ1 T cells as clinically actionable mediators of ICB response in major histocompatibility complex-high and -low MCCs, respectively.

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Palmoplantar pustulosis (PPP) is a chronic inflammatory recurrent disease characterized by sterile pustules involving palms and/or feet. Presently, there are no standard recommended treatment regimens. Tofacitinib is an oral Janus kinase (JAK) inhibitor, mainly acts on JAK 1 and 3 and has been approved for the treatment of rheumatoid arthritis in adults. Herein, we present a case of a patient with PPP who did not respond to IL-17A inhibitor secukinumab but was successfully treated by the JAK inhibitor tofacitinib.

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Constructed wetlands use vegetation and microorganisms to remove contaminants like nitrogen and phosphorus from water. For mariculture, the impact of salinity on the efficiency of wastewater treatment of wetlands is unneglectable. However, little is known about their impact on the microbiome in constructed wetlands. Here, we set four salinity levels (15, 22, 29, and 36) in Salicornia constructed wetlands, and the experiment was conducted for a period of 72 days. The 15 group exhibited the highest removal rates of nitrogen compounds and phosphate, compared to the other salinity groups, the nosZ gene exhibited significantly higher expression in the 22 group (p < 0.05), indicated that microorganisms in 22 salinity have higher denitrification abilities. The three dominant phyla identified within the microbiomes were Proteobacteria, known for their diverse metabolic capabilities; Cyanobacteria, important for photosynthesis and nitrogen fixation; and Firmicutes, which include many fermenters. The ecological network analysis revealed a 'small world' model, characterized by high interconnectivity and short path lengths between microbial species, and had higher co-occurrence (45.13%) observed in this study comparing to the Erdös-Réyni random one (32.35%). The genus Microbulbifer emerged as the sole connector taxon, pivotal for integrating different microbial communities involved in nitrogen removal. A negative correlation was observed between salinity levels and network complexity, as assessed by the number of connections and diversity of interactions within the microbial community. Collectively, these findings underscore the critical role of microbial community interactions in optimizing nitrogen removal in constructed wetlands, with potential applications in the design and management of such systems for improved wastewater treatment in mariculture.

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STUDY DESIGN: Retrospective study. OBJECTIVES: The objective of this investigation was to formulate and internally verify a customized machine learning (ML) framework for forecasting cerebrospinal fluid leakage (CSFL) in lumbar fusion surgery. This was accomplished by integrating imaging parameters and using the SHapley Additive exPlanation (SHAP) technique to elucidate the interpretability of the model. SUMMARY OF BACKGROUND DATA: Given the increasing incidence and surgical volume of spinal degeneration worldwide, accurate predictions of postoperative complications are urgently needed. SHAP-based interpretable ML models have not been used for CSFL risk factor analysis in lumbar fusion surgery. METHODS: Clinical and imaging data were retrospectively collected from 3505 patients who underwent lumbar fusion surgery. Six distinct machine learning models were formulated: extreme gradient boosting (XGBoost), decision tree (DT), random forest (RF), support vector machine (SVM), Gaussian naive Bayes (GaussianNB), and K-nearest neighbors (KNN) models. Evaluation of model performance on the test dataset was performed using performance metrics, and the analysis was executed through the SHAP framework. RESULTS: CSFL was detected in 95 (2.71%) of 3505 patients. Notably, the XGBoost model exhibited outstanding accuracy in forecasting CSFLs, with high precision (0.9815), recall (0.6667), accuracy (0.8182), F1 score (0.7347), and AUC (0.7343). In addition, through SHAP analysis, significant predictors of CSFL were identified, including ligamentum flavum thickness, zygapophysial joint degeneration grade, central spinal stenosis grade, decompression segment count, decompression mode, intervertebral height difference, Cobb angle, intervertebral height index difference, operation mode, lumbar segment lordosis angle difference, Meyerding grade of lumbar spondylolisthesis, and revision surgery. CONCLUSIONS: The combination of the XGBoost model with the SHAP is an effective tool for predicting the risk of CSFL during lumbar fusion surgery. Its implementation could aid clinicians in making informed decisions, potentially enhancing patient outcomes and lowering healthcare expenses. This study advocates for the adoption of this approach in clinical settings to enhance the evaluation of CSFL risk among patients undergoing lumbar fusion.

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Companion diagnostics using biomarkers have gained prominence in guiding radiotherapy. However, biopsy-based techniques fail to account for real-time variations in target response and tumor heterogeneity. Herein, we design an activated afterglow/MRI probe as a companion diagnostics tool for dynamically assessing biomarker apurinic/apyrimidinic endonuclease 1(APE1) during radiotherapy in vivo. We employ ultrabright afterglow nanoparticles and ultrasmall FeMnOx nanoparticles as dual contrast agents, significantly broadening signal change range and enhancing the sensitivity of APE1 imaging (limit of detection: 0.0092 U/mL in afterglow imaging and 0.16 U/mL in MRI). We devise longitudinally and transversely subtraction-enhanced imaging (L&T-SEI) strategy to markedly enhance MRI contrast and signal-to-noise ratio between tumor and normal tissue of living female mice. The combined afterglow and MRI facilitate both anatomical and functional imaging of APE1 activity. This probe enables correlation of afterglow and MRI signals with APE1 expression, radiation dosage, intratumor ROS, and DNA damage, enabling early prediction of radiotherapy outcomes (as early as 3 h), significantly preceding tumor size reduction (6 days). By monitoring APE1 levels, this probe allows for early and sensitive detection of liver organ injury, outperforming histopathological analysis. Furthermore, MRI evaluates APE1 expression in radiation-induced abscopal effects provides insights into underlying mechanisms, and supports the development of treatment protocols.

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Alzheimer's Disease (AD) is a complex neurodegenerative disorder significantly influenced by sex differences, with approximately two-thirds of AD patients being women. Characterizing the sex-specific AD progression and identifying its progression trajectory is a crucial step to developing effective risk stratification and prevention strategies. In this study, we developed an autoencoder to uncover sex-specific sub-phenotypes in AD progression leveraging longitudinal electronic health record (EHR) data from OneFlorida+ Clinical Research Consortium. Specifically, we first constructed temporal patient representation using longitudinal EHRs from a sex-stratified AD cohort. We used a long short-term memory (LSTM)-based autoencoder to extract and generate latent representation embeddings from sequential clinical records of patients. We then applied hierarchical agglomerative clustering to the learned representations, grouping patients based on their progression sub-phenotypes. The experimental results show we successfully identified five primary sex-based AD sub-phenotypes with corresponding progression pathways with high confidence. These sex-specific sub-phenotypes not only illustrated distinct AD progression patterns but also revealed differences in clinical characteristics and comorbidities between females and males in AD development. These findings could provide valuable insights for advancing personalized AD intervention and treatment strategies.

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To establish a rapid real-time RT-PCR method for differentiating wild-type classical swine fever virus (CSFV) strains from vaccine strains (HCLV), we designed a universal primer targeting the NS3 gene to detect wild-type CSFV strains and vaccine strains simultaneously, and two TaqMan-MGB probes were designed to differentiate between wild-type and vaccine strains. After optimizing the RT-qPCR conditions, a rapid dual TaqMan-MGB RT-qPCR method for the detection and identification of CSFV and HCLV was developed. The results showed that method could specifically detect CSFV and HCLV with no cross-reactivity with other swine pathogens. The analytic sensitivity for the NS3 gene of CSFV and HCLV were 1.67 × 101 copies/µL, respectively. For precision testing, the repeatability and reproducibility of the test was less than 2%. This method was successfully used for the rapid detection of 193 biological samples collected from CSFV-vaccinated pigs. This fast and accurate detection technology can be used for the detection of CSFV and is suitable for differentiating between wild-type CSFV strains and vaccine strains.

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The specificity and clinical relevance of cancer-associated fibroblasts (CAFs) in prostate cancer (PCa), as well as the effect of androgen deprivation therapy (ADT) on CAFs, remain to be fully elucidated. Using cell lineage diversity and weighted gene co-expression network analysis (WGCNA), we pinpointed a unique CAF signature exclusive to PCa. The specificity of this CAF signature was validated through single-cell RNA sequencing (scRNA-seq), cell line RNA sequencing, and immunohistochemistry. This signature associates CAFs with tumor progression, elevated Gleason scores, and the emergence of castration resistant prostate cancer (CRPC). Using scRNA-seq on collected samples, we demonstrated that the CAF-specific signature is not altered by ADT, maintaining its peak signal output. Identifying a PCa-specific CAF signature and observing signaling changes in CAFs after ADT lay essential groundwork for further PCa studies.

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BACKGROUND: COVID-19 is a new infectious disease caused by the severe acute respiratory syndrome coronavirus 2 (SARS CoV-2). Since the outbreak in December 2019, it has caused an unprecedented world pandemic, leading to a global human health crisis. Although SARS CoV-2 mainly affects the lungs, causing interstitial pneumonia and severe acute respiratory distress syndrome, a number of patients often have extensive clinical manifestations, such as gastrointestinal symptoms, cardiovascular damage and renal dysfunction. PURPOSE: This review article discusses the pathogenic mechanisms of cardiovascular damage in COVID-19 patients and provides some useful suggestions for future clinical diagnosis, treatment and prevention. METHODS: An English-language literature search was conducted in PubMed and Web of Science databases up to 12th April, 2024 for the terms "COVID-19", "SARS CoV-2", "cardiovascular damage", "myocardial injury", "myocarditis", "hypertension", "arrhythmia", "heart failure" and "coronary heart disease", especially update articles in 2023 and 2024. Salient medical literatures regarding the cardiovascular damage of COVID-19 were selected, extracted and synthesized. RESULTS: The most common cardiovascular damage was myocarditis and pericarditis, hypertension, arrhythmia, myocardial injury and heart failure, coronary heart disease, stress cardiomyopathy, ischemic stroke, blood coagulation abnormalities, and dyslipidemia. Two important pathogenic mechanisms of the cardiovascular damage may be direct viral cytotoxicity as well as indirect hyperimmune responses of the body to SARS CoV-2 infection. CONCLUSIONS: Cardiovascular damage in COVID-19 patients is common and portends a worse prognosis. Although the underlying pathophysiological mechanisms of cardiovascular damage related to COVID-19 are not completely clear, two important pathogenic mechanisms of cardiovascular damage may be the direct damage of the SARSCoV-2 infection and the indirect hyperimmune responses.

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Functional traits are indicators of the responses and adaptation of organisms to environmental changes and cascade to a series of ecosystem functions. The functional traits of soil animals are sensitive to environmental factors and may characterize and predict the changes of ecosystem functions. Multiple dimensions of biodiversity that combing species, phylogenetic, and functional diversity improves the understanding of distribution patterns, community assembly mechanisms and ecosystem functions of soil animals. In this review, we listed the categories of soil animal functional traits and their ecological significance, and summarized current researches on the responses of soil animal communities to environmental changes and the community assembly processes based on trait-based approaches. We proposed to strengthen the study on the impacts of eco-evolution processes of biotic interactions to soil animal functional traits, establish the database of soil animal functional traits, and apply trait-based approaches in the ecological restoration in the future, which would benefit soil biodiversity conservation and sustainability of soil ecosystems.

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The currently dominant types of land management are threatening the multifunctionality of ecosystems, which is vital for human well-being. Here, we present a novel ecological-economic assessment of how multifunctionality of agroecosystems in Central Germany depends on land-use type and climate. Our analysis includes 14 ecosystem variables in a large-scale field experiment with five different land-use types under two different climate scenarios (ambient and future climate). We consider ecological multifunctionality measures using averaging approaches with different weights, reflecting preferences of four relevant stakeholders based on adapted survey data. Additionally, we propose an economic multifunctionality measure based on the aggregate economic value of ecosystem services. Results show that intensive management and future climate decrease ecological multifunctionality for most scenarios in both grassland and cropland. Only under a weighting based on farmers' preferences, intensively-managed grassland shows higher multifunctionality than sustainably-managed grassland. The economic multifunctionality measure is about ~1.7 to 1.9 times higher for sustainable, compared to intensive, management for both grassland and cropland. Soil biodiversity correlates positively with ecological multifunctionality and is expected to be one of its drivers. As the currently prevailing land management provides high multifunctionality for farmers, but not for society at large, we suggest to promote and economically incentivise sustainable land management that enhances both ecological and economic multifunctionality, also under future climatic conditions.

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Cardiovascular diseases (CVDs) have emerged as a predominant threat to human health, surpassing the incidence and mortality rates of neoplastic diseases. Extracellular vesicles (EVs) serve as vital mediators in intercellular communication and material exchange. Endothelial progenitor cells (EPCs), recognized as precursors of vascular endothelial cells (ECs), have garnered considerable attention in recent years due to the potential therapeutic value of their derived extracellular vesicles (EPC-EVs) in the context of CVDs. This comprehensive review systematically explores the origins, characteristics, and functions of EPCs, alongside the classification, properties, biogenesis, and extraction techniques of EVs, with particular emphasis on their protective roles in CVDs. Additionally, we delve into the essential bioactive components of EPC-EVs, including microRNAs, long non-coding RNAs, and proteins, analyzing their beneficial effects in promoting angiogenesis, anti-inflammatory and anti-oxidant activities, anti-fibrosis, anti-apoptosis, and myocardial regeneration. Furthermore, this review comprehensively investigates the therapeutic potential of EPC-EVs across various CVDs, encompassing acute myocardial infarction, myocardial ischemia-reperfusion injury, atherosclerosis, non-ischemic cardiomyopathies, and diabetic cardiovascular disease. Lastly, we summarize the potential challenges associated with the clinical application of EPC-EVs and outline future directions, aiming to offer a valuable resource for both theoretical insights and practical applications of EPC-EVs in managing CVDs.

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Background: Breast-conserving surgery (BCS) stands as the favored modality for treating early-stage breast cancer. Accurately forecasting the feasibility of BCS preoperatively can aid in surgical planning and reduce the rate of switching of surgical methods and reoperation. The objective of this study is to identify the radiomics features and preoperative breast magnetic resonance imaging (MRI) characteristics that are linked with positive margins following BCS in patients with breast cancer, with the ultimate aim of creating a predictive model for the feasibility of BCS. Methods: This study included a cohort of 221 pretreatment MRI images obtained from patients with breast cancer. A total of seven MRI semantic features and 1,561 radiomics features of lesions were extracted. The feature subset was determined by eliminating redundancy and correlation based on the features of the training set. The least absolute shrinkage and selection operator (LASSO) logistic regression was then trained with this subset to classify the final BCS positive and negative margins and subsequently validated using the test set. Results: Seven features were significant in the discrimination of cases achieving positive and negative margins. The radiomics signature achieved area under the curve (AUC), accuracy, sensitivity, and specificity of 0.760 [95% confidence interval (CI): 0.630, 0.891], 0.712 (95% CI: 0.569, 0.829), 0.882 (95% CI: 0.623, 0.979) and 0.629 (95% CI: 0.449, 0.780) in the test set, respectively. The combined model of radiomics signature and background parenchymal enhancement (BPE) demonstrated an AUC, accuracy, sensitivity, and specificity of 0.759 (95% CI: 0.628, 0.890), 0.654 (95% CI: 0.509, 0.780), 0.679 (95% CI: 0.476, 0.834) and 0.625 (95% CI: 0.408, 0.804). Conclusions: The combination of preoperative MRI radiomics features can well predict the success of breast conserving surgery.